Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [104]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [105]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[105]:
<matplotlib.image.AxesImage at 0xb30e92550>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [106]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[106]:
<matplotlib.image.AxesImage at 0xb2f463c88>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [107]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.8.0
/Users/shakiralharthi/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [108]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32, [None, image_width,image_height, image_channels], name='real_input')
    z_input = tf.placeholder(tf.float32,[None, z_dim], name='z_input')
    lr = tf.placeholder(tf.float32, name= 'lr')

    return real_input, z_input, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed
In [109]:
#set alpha 

alpha = .2

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [141]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    #print(images.shape)
    with tf.variable_scope('discriminator',reuse=reuse):
        x = tf.layers.conv2d(inputs=images, filters = 32 , kernel_size= 5, strides= 2 ,  padding='same',activation= None)
        relul = tf.maximum(x * alpha, x)
        
   
        #now shape is 14*14*32
        
        x2 = tf.layers.conv2d(relul, filters = 64, kernel_size= 5, strides=2, padding = 'same',activation=None)
        x2 = tf.layers.batch_normalization(x2, training= True)
        relul2 = tf.maximum(x2 * alpha, x2)
        #relul2 = tf.layers.dropout(relul2,rate=.8)
        
        # Now shape is 7*7*64

        x3 =  tf.layers.conv2d(relul2, filters = 128, kernel_size= 5, strides=2, padding = 'same',activation=None)
        x3 =  tf.layers.batch_normalization(x3, training= True)
        relul3 = tf.maximum(x3 * alpha, x3)
        #relul3 = tf.layers.dropout(relul3,rate=.8)
        
        
       
        #print('relul3  =',relul3.shape)
        

        
        flat = tf.reshape(relul3, (-1, 4*4*128))
        
        logits = tf.layers.dense(flat, 1)
        
        output = tf.sigmoid(logits)
        
        
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [113]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
   
    # TODO: Implement Function

    reuse = False if is_train else True
    with tf.variable_scope('generator', reuse = reuse):
        
        x1= tf.layers.dense(z,2*2*1024)
        #print(x1.shape)
        x1= tf.reshape(z, (-1, 2,2,1024))
        x1= tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        #print (x1.shape)
        x2 = tf.layers.conv2d_transpose(x1, 512,5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        #print(x2.shape)
        
        x3 = tf.layers.conv2d_transpose(x2, 256,5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        #print(x3.shape)
        
        
        

        
        #print(out_channel_dim)
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x
        
        #print(logits.shape, 'logits shape')
        output = tf.tanh(logits)
    
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [114]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_output = generator(input_z, out_channel_dim, is_train=True)
    
    d_output_real, d_logit_real = discriminator(input_real, reuse= False)
    d_output_fake, d_logit_fake = discriminator(g_output, reuse= True)
    
    
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logit_real, labels=tf.ones_like(d_output_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logit_fake, labels=tf.zeros_like(d_output_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logit_fake,labels = tf.ones_like(d_output_fake)))
    
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [115]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [122]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-.5, .5, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [142]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
  

    
    #print(data_shape)
    step = 0
    real_input,z_input,lr = model_inputs(data_shape[1],data_shape[2],data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(real_input, z_input, data_shape[3])
    
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                #print(batch_images.shape)
                step += 1
                # TODO: Train Model
                batch_z = np.random.uniform(-.5,.5 , size=(batch_size, z_dim))

                #print('batch_z shape',batch_z.shape)
                
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict = {real_input: batch_images, z_input: batch_z})
                _ = sess.run(g_train_opt, feed_dict = {z_input: batch_z, real_input: batch_images})

                if step % 90 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
               
                    show_generator_output(sess, 512, z_input, data_shape[3], data_image_mode)
    
                   
                

Below I will try the model on multiple different hyperparameter values on the MNIST images

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [127]:
batch_size =160
z_dim = 128
learning_rate = 0.00005
beta1 = .5
alpha= .3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    
print('finsihed')
Epoch 1/3... Discriminator Loss: 4.8456... Generator Loss: 0.0127
Epoch 1/3... Discriminator Loss: 2.1635... Generator Loss: 0.1884
Epoch 1/3... Discriminator Loss: 1.4470... Generator Loss: 0.4051
Epoch 1/3... Discriminator Loss: 1.1887... Generator Loss: 0.7038
Epoch 1/3... Discriminator Loss: 0.9263... Generator Loss: 0.9077
Epoch 1/3... Discriminator Loss: 1.2723... Generator Loss: 0.8783
Epoch 1/3... Discriminator Loss: 0.8146... Generator Loss: 1.0887
Epoch 1/3... Discriminator Loss: 0.7756... Generator Loss: 1.0613
Epoch 1/3... Discriminator Loss: 0.9586... Generator Loss: 0.9960
Epoch 1/3... Discriminator Loss: 0.9379... Generator Loss: 1.2021
Epoch 1/3... Discriminator Loss: 0.7609... Generator Loss: 1.1621
Epoch 1/3... Discriminator Loss: 0.8312... Generator Loss: 0.9976
Epoch 2/3... Discriminator Loss: 0.6578... Generator Loss: 1.2964
Epoch 2/3... Discriminator Loss: 0.4373... Generator Loss: 1.6830
Epoch 2/3... Discriminator Loss: 0.4297... Generator Loss: 1.8060
Epoch 2/3... Discriminator Loss: 0.4587... Generator Loss: 1.5202
Epoch 2/3... Discriminator Loss: 0.2156... Generator Loss: 2.4392
Epoch 2/3... Discriminator Loss: 0.2117... Generator Loss: 2.3788
Epoch 2/3... Discriminator Loss: 0.6900... Generator Loss: 1.0257
Epoch 2/3... Discriminator Loss: 0.3572... Generator Loss: 1.7977
Epoch 2/3... Discriminator Loss: 0.5801... Generator Loss: 1.4291
Epoch 2/3... Discriminator Loss: 0.2263... Generator Loss: 2.3495
Epoch 2/3... Discriminator Loss: 0.5017... Generator Loss: 1.4805
Epoch 2/3... Discriminator Loss: 0.2776... Generator Loss: 2.0380
Epoch 2/3... Discriminator Loss: 0.1302... Generator Loss: 2.6467
Epoch 3/3... Discriminator Loss: 0.1403... Generator Loss: 2.3698
Epoch 3/3... Discriminator Loss: 0.1112... Generator Loss: 2.7575
Epoch 3/3... Discriminator Loss: 0.1671... Generator Loss: 2.3139
Epoch 3/3... Discriminator Loss: 0.1826... Generator Loss: 2.2372
Epoch 3/3... Discriminator Loss: 0.1863... Generator Loss: 2.2974
Epoch 3/3... Discriminator Loss: 0.1807... Generator Loss: 2.4494
Epoch 3/3... Discriminator Loss: 0.0962... Generator Loss: 2.8779
Epoch 3/3... Discriminator Loss: 0.0775... Generator Loss: 3.3679
Epoch 3/3... Discriminator Loss: 0.1060... Generator Loss: 2.7401
Epoch 3/3... Discriminator Loss: 0.1453... Generator Loss: 2.3169
Epoch 3/3... Discriminator Loss: 0.1541... Generator Loss: 2.1264
Epoch 3/3... Discriminator Loss: 0.2396... Generator Loss: 1.6929
finsihed
In [128]:
batch_size =160
z_dim = 128
learning_rate = 0.0001
beta1 = .5
alpha= .3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    
print('finsihed')
Epoch 1/3... Discriminator Loss: 2.1277... Generator Loss: 0.1847
Epoch 1/3... Discriminator Loss: 0.4366... Generator Loss: 1.4987
Epoch 1/3... Discriminator Loss: 0.3527... Generator Loss: 1.5210
Epoch 1/3... Discriminator Loss: 0.2433... Generator Loss: 2.2662
Epoch 1/3... Discriminator Loss: 0.2875... Generator Loss: 1.9841
Epoch 1/3... Discriminator Loss: 0.1010... Generator Loss: 3.0715
Epoch 1/3... Discriminator Loss: 0.2242... Generator Loss: 2.0155
Epoch 1/3... Discriminator Loss: 0.2610... Generator Loss: 2.2627
Epoch 1/3... Discriminator Loss: 0.1216... Generator Loss: 3.1276
Epoch 1/3... Discriminator Loss: 0.3328... Generator Loss: 2.0169
Epoch 1/3... Discriminator Loss: 0.3853... Generator Loss: 1.6573
Epoch 1/3... Discriminator Loss: 0.2735... Generator Loss: 2.3240
Epoch 2/3... Discriminator Loss: 0.1575... Generator Loss: 2.5828
Epoch 2/3... Discriminator Loss: 0.0963... Generator Loss: 2.9911
Epoch 2/3... Discriminator Loss: 0.1404... Generator Loss: 2.6364
Epoch 2/3... Discriminator Loss: 0.1302... Generator Loss: 2.8152
Epoch 2/3... Discriminator Loss: 0.0416... Generator Loss: 3.6855
Epoch 2/3... Discriminator Loss: 0.0768... Generator Loss: 3.1092
Epoch 2/3... Discriminator Loss: 0.0611... Generator Loss: 3.3987
Epoch 2/3... Discriminator Loss: 0.0349... Generator Loss: 3.7883
Epoch 2/3... Discriminator Loss: 0.0240... Generator Loss: 4.0570
Epoch 2/3... Discriminator Loss: 0.0309... Generator Loss: 3.8990
Epoch 2/3... Discriminator Loss: 0.0715... Generator Loss: 3.0446
Epoch 2/3... Discriminator Loss: 0.0604... Generator Loss: 3.3695
Epoch 2/3... Discriminator Loss: 0.1520... Generator Loss: 2.0817
Epoch 3/3... Discriminator Loss: 2.5869... Generator Loss: 0.0898
Epoch 3/3... Discriminator Loss: 0.4187... Generator Loss: 2.3835
Epoch 3/3... Discriminator Loss: 0.6140... Generator Loss: 4.0309
Epoch 3/3... Discriminator Loss: 1.7852... Generator Loss: 0.2259
Epoch 3/3... Discriminator Loss: 0.2073... Generator Loss: 2.4665
Epoch 3/3... Discriminator Loss: 0.3182... Generator Loss: 2.7030
Epoch 3/3... Discriminator Loss: 0.3664... Generator Loss: 1.8556
Epoch 3/3... Discriminator Loss: 0.4446... Generator Loss: 1.5181
Epoch 3/3... Discriminator Loss: 0.4122... Generator Loss: 1.6137
Epoch 3/3... Discriminator Loss: 0.4510... Generator Loss: 1.6342
Epoch 3/3... Discriminator Loss: 0.3955... Generator Loss: 1.6987
Epoch 3/3... Discriminator Loss: 0.3470... Generator Loss: 1.9679
finsihed
In [129]:
batch_size =160
z_dim = 128
learning_rate = 0.0001
beta1 = .4
alpha= .3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    
print('finsihed')
Epoch 1/3... Discriminator Loss: 2.1278... Generator Loss: 0.2006
Epoch 1/3... Discriminator Loss: 0.6342... Generator Loss: 1.1269
Epoch 1/3... Discriminator Loss: 0.7374... Generator Loss: 0.9682
Epoch 1/3... Discriminator Loss: 0.3540... Generator Loss: 1.8842
Epoch 1/3... Discriminator Loss: 0.6056... Generator Loss: 1.2383
Epoch 1/3... Discriminator Loss: 0.3861... Generator Loss: 1.6338
Epoch 1/3... Discriminator Loss: 0.9761... Generator Loss: 0.9069
Epoch 1/3... Discriminator Loss: 0.8879... Generator Loss: 0.9654
Epoch 1/3... Discriminator Loss: 0.9691... Generator Loss: 0.9790
Epoch 1/3... Discriminator Loss: 0.2790... Generator Loss: 2.3239
Epoch 1/3... Discriminator Loss: 0.2509... Generator Loss: 2.3521
Epoch 1/3... Discriminator Loss: 0.1969... Generator Loss: 2.1145
Epoch 2/3... Discriminator Loss: 0.3063... Generator Loss: 2.0163
Epoch 2/3... Discriminator Loss: 0.3028... Generator Loss: 1.7522
Epoch 2/3... Discriminator Loss: 0.1004... Generator Loss: 3.1574
Epoch 2/3... Discriminator Loss: 0.2319... Generator Loss: 2.0357
Epoch 2/3... Discriminator Loss: 0.0831... Generator Loss: 3.0528
Epoch 2/3... Discriminator Loss: 0.1445... Generator Loss: 2.5445
Epoch 2/3... Discriminator Loss: 0.0457... Generator Loss: 3.6598
Epoch 2/3... Discriminator Loss: 0.0350... Generator Loss: 3.9560
Epoch 2/3... Discriminator Loss: 0.0295... Generator Loss: 4.0516
Epoch 2/3... Discriminator Loss: 0.0344... Generator Loss: 3.9972
Epoch 2/3... Discriminator Loss: 0.0726... Generator Loss: 3.0613
Epoch 2/3... Discriminator Loss: 0.0468... Generator Loss: 3.4775
Epoch 2/3... Discriminator Loss: 0.0177... Generator Loss: 4.4268
Epoch 3/3... Discriminator Loss: 0.0216... Generator Loss: 4.1540
Epoch 3/3... Discriminator Loss: 0.0345... Generator Loss: 3.6693
Epoch 3/3... Discriminator Loss: 0.0269... Generator Loss: 4.0007
Epoch 3/3... Discriminator Loss: 0.0325... Generator Loss: 3.9035
Epoch 3/3... Discriminator Loss: 0.0303... Generator Loss: 3.7524
Epoch 3/3... Discriminator Loss: 0.0307... Generator Loss: 3.8434
Epoch 3/3... Discriminator Loss: 0.0593... Generator Loss: 2.9632
Epoch 3/3... Discriminator Loss: 0.0324... Generator Loss: 3.7463
Epoch 3/3... Discriminator Loss: 0.0404... Generator Loss: 3.6366
Epoch 3/3... Discriminator Loss: 0.6391... Generator Loss: 0.8177
Epoch 3/3... Discriminator Loss: 0.6108... Generator Loss: 1.6335
Epoch 3/3... Discriminator Loss: 0.9999... Generator Loss: 3.5986
finsihed
In [143]:
batch_size =160
z_dim = 128
learning_rate = 0.0001
beta1 = .5
alpha= .2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
    
print('finsihed')
Epoch 1/3... Discriminator Loss: 0.4575... Generator Loss: 1.3059
Epoch 1/3... Discriminator Loss: 1.0181... Generator Loss: 1.0043
Epoch 1/3... Discriminator Loss: 0.3522... Generator Loss: 1.6218
Epoch 1/3... Discriminator Loss: 0.2353... Generator Loss: 2.2852
Epoch 2/3... Discriminator Loss: 0.0939... Generator Loss: 3.1495
Epoch 2/3... Discriminator Loss: 0.0496... Generator Loss: 3.5308
Epoch 2/3... Discriminator Loss: 0.0844... Generator Loss: 3.2576
Epoch 2/3... Discriminator Loss: 0.0762... Generator Loss: 2.8548
Epoch 3/3... Discriminator Loss: 1.5946... Generator Loss: 0.5611
Epoch 3/3... Discriminator Loss: 0.4995... Generator Loss: 1.5474
Epoch 3/3... Discriminator Loss: 0.4379... Generator Loss: 1.6843
Epoch 3/3... Discriminator Loss: 0.2864... Generator Loss: 2.3150
finsihed

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [144]:
batch_size = 160
z_dim = 128
learning_rate = .0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 3

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
    
    
print('finished')
Epoch 1/3... Discriminator Loss: 2.5644... Generator Loss: 0.3356
Epoch 1/3... Discriminator Loss: 0.8476... Generator Loss: 1.1397
Epoch 1/3... Discriminator Loss: 0.6462... Generator Loss: 1.4170
Epoch 1/3... Discriminator Loss: 0.5099... Generator Loss: 1.6445
Epoch 1/3... Discriminator Loss: 0.4837... Generator Loss: 1.7918
Epoch 1/3... Discriminator Loss: 0.4344... Generator Loss: 1.2608
Epoch 1/3... Discriminator Loss: 0.2856... Generator Loss: 2.6357
Epoch 1/3... Discriminator Loss: 1.7816... Generator Loss: 0.2317
Epoch 1/3... Discriminator Loss: 0.5678... Generator Loss: 1.7014
Epoch 1/3... Discriminator Loss: 0.4564... Generator Loss: 1.8889
Epoch 1/3... Discriminator Loss: 0.2581... Generator Loss: 2.0852
Epoch 1/3... Discriminator Loss: 0.2531... Generator Loss: 2.4033
Epoch 1/3... Discriminator Loss: 0.1096... Generator Loss: 3.0875
Epoch 1/3... Discriminator Loss: 0.1792... Generator Loss: 2.7483
Epoch 2/3... Discriminator Loss: 0.1497... Generator Loss: 2.8235
Epoch 2/3... Discriminator Loss: 0.0961... Generator Loss: 3.1603
Epoch 2/3... Discriminator Loss: 0.1481... Generator Loss: 2.2004
Epoch 2/3... Discriminator Loss: 0.1604... Generator Loss: 2.8764
Epoch 2/3... Discriminator Loss: 0.1116... Generator Loss: 3.1101
Epoch 2/3... Discriminator Loss: 0.0790... Generator Loss: 3.4778
Epoch 2/3... Discriminator Loss: 0.2081... Generator Loss: 2.0500
Epoch 2/3... Discriminator Loss: 0.0630... Generator Loss: 3.4219
Epoch 2/3... Discriminator Loss: 0.0990... Generator Loss: 3.1424
Epoch 2/3... Discriminator Loss: 0.0551... Generator Loss: 3.9795
Epoch 2/3... Discriminator Loss: 0.0206... Generator Loss: 4.5077
Epoch 2/3... Discriminator Loss: 0.0562... Generator Loss: 3.8708
Epoch 2/3... Discriminator Loss: 0.0286... Generator Loss: 4.6281
Epoch 2/3... Discriminator Loss: 0.0415... Generator Loss: 3.7075
Epoch 3/3... Discriminator Loss: 0.0280... Generator Loss: 4.3547
Epoch 3/3... Discriminator Loss: 0.0252... Generator Loss: 4.5826
Epoch 3/3... Discriminator Loss: 0.0382... Generator Loss: 3.7339
Epoch 3/3... Discriminator Loss: 0.0368... Generator Loss: 3.9645
Epoch 3/3... Discriminator Loss: 0.0214... Generator Loss: 4.7200
Epoch 3/3... Discriminator Loss: 0.0180... Generator Loss: 4.5078
Epoch 3/3... Discriminator Loss: 0.0092... Generator Loss: 5.3477
Epoch 3/3... Discriminator Loss: 0.0168... Generator Loss: 4.8330
Epoch 3/3... Discriminator Loss: 0.0260... Generator Loss: 4.7466
Epoch 3/3... Discriminator Loss: 0.0128... Generator Loss: 5.1522
Epoch 3/3... Discriminator Loss: 0.0133... Generator Loss: 4.9163
Epoch 3/3... Discriminator Loss: 0.0052... Generator Loss: 5.6378
Epoch 3/3... Discriminator Loss: 0.0053... Generator Loss: 5.4526
Epoch 3/3... Discriminator Loss: 0.0020... Generator Loss: 6.4204
finished

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.